Fraud And Abuse Operations Analyst

Responsibilities

• Participate in the abuse on-call rotation, responding to and resolving fraud and abuse events to protect users and customers.
• Investigate fraud and abuse claims from diverse sources, partnering with senior teammates on complex cases; findings inform decisions and strategies to prevent future incidents and minimize losses.
• Proactively perform threat modeling of abuse surfaces and continuously survey external fraud trends, adversary techniques, tooling, and emerging threat vectors.
• Help triage and manage fraud and abuse events as internal incidents (SEVs), collaborating across teams to drive resolution and minimize disruption.
• Partner with Consumer Risk and other product teams, providing operational insights from on-call and investigations to inform development of effective fraud mitigation solutions.

Qualifications

• 3+ years of experience in fraud/abuse operations, trust & safety, and/or incident response.
• Experience triaging fraud or related incidents with tight containment deadlines.
• Experience managing escalations and making informed trade-off decisions with multiple stakeholders.
• Ability to coordinate and communicate effectively with internal Legal, Compliance, Comms and other risk management teams during incident triage.
• Battle-tested mental model for balancing fraud mitigation vs. conversion, calibrated to Plaid’s business objectives under pressure.
• Working knowledge of fraud typologies (account takeover, payments fraud, ACH fraud, chargebacks, kiting, rewards harvesting, etc.).
• Familiarity with TTPs used by bad actors to commit fraud on the web and common mitigation strategies (with pros/cons).
• Dark and grey-web navigation and investigation experience; ability to assess source credibility and translate external intelligence into actionable insights.
• Experience translating fraud investigations and research into actionable detection signals, rules, and labeled datasets; partnering with DS/ML and product teams to improve feature sets, labeling quality, evaluation frameworks, and model decay monitoring.
• Experience developing and maintaining threat models for authentication and other high-risk product surfaces, with a track record of proactively identifying emerging risks.
• Proficiency with SQL for querying data sets and building reports.
• Comfortable with common analysis tools and manipulation tools.
• Ability to spot anomalies and fraudulent patterns in logs and other data sets (transactions, request data, etc.), with judgment to escalate ambiguous cases.
• Experience contributing improvements to operational processes and controls.
• Strong written and verbal communication skills.

Nice-to-Haves

• Familiarity with machine learning models for fraud detection.
• Fraud domain certifications (e.g., CFE/fraud examination background).
• Exposure to production ML model lifecycles and metrics for drift/decay.
• Effective use of AI and AI agents to accelerate investigations and research.